The ability to develop predictive computational models that capture cellular responses to changes in their environment is a central goal of modern cellular biology. Bacterial chemotaxis is among the best-studied signal transduction pathways, where decades of analysis of structural, biochemical, genetic and physiological components of signaling have contributed to a broad understanding of the overall signaling process. Bacteria sense many of the changes in their local chemical environment by the binding of ligands to a family of chemotaxis receptors, which in turn trigger the activation of a signaling pathway that ultimately regulates the rotation of the flagellar motor. A detailed understanding of the spatial and temporal architecture of the bacterial apparatus for chemotaxis is a problem of fundamental interest because it will provide a framework to integrate the extensive genetic, physiological, biochemical and structural analyses of this process. With the advent of advanced imaging methods that allow spatial localization of specific protein complexes within the cell, the prospect of developing an integrated structural understanding of whole bacterial cells at the molecular level is potentially within range. Chemotaxis is a particularly tractable signaling pathway given the small number of components involved and the knowledge of the spatial localization of the front end of the signal transduction cascade. Over the last few years, we have taken systematic steps to bridge the gap from structure to physiology as it relates to deriving a quantitative model for chemotaxis that takes into account the spatial and molecular organization of the protein components in the signaling pathway. We began with first establishing the feasibility of localizing the receptors in the bacterial cell, and moved on to establishing the possibility of obtaining 3D structures of receptor proteins when they are still in an intact bacterium. This in turn, led to the discovery of the partially ordered hexagonal arrangement of receptors in the plane of the membrane. We have now extended these foundations to translating the information on the structure of the receptors, their spatial distribution, and richness of the growth medium into a testable, predictive computational model for chemotaxis signaling. The ability of the chemoreceptor assembly to amplify the signals derived from ligand binding, and the allosteric nature of the chemotaxis response, represent two unique features of the bacterial chemotaxis signaling pathway. Thus, activation of the CheA kinase in the cytoplasm can be 100 times higher than the change in ligand occupancy of the periplasmic domain of the receptor. The allosteric response is believed to arise from the cooperative interactions of the chemoreceptor assembly, with the slope of the dose-response curve determining the sensitivity of the overall response. Understanding the mechanisms of the cooperativity and signal amplification are therefore questions of fundamental interest in this field. We are addressing this problem using (i) cryo-electron tomography to explore key physical elements important in the organization of the chemoreceptor array, (ii) computational studies that use the tomographic information to generate predictive models for the signaling response, and (iii) FRET experiments to experimentally test these theoretical predictions in vivo under the same conditions as those used for the tomographic experiments. Our results demonstrate that changes in lateral packing densities of the partially ordered, spatially extended chemoreceptor arrays can modulate the bacterial chemotaxis response, and that information on the molecular organization of the arrays derived by cryo-electron tomography of intact cells can be translated into testable, predictive computational models. The major discovery here is that lateral packing density can be shown to be a key variable that can be parameterized to develop a predictive, quantitative computational model for chemotaxis, and that the nature and extent of the bacterial physiological response can be predicted to a good approximation using parameters derived from the tomographic data. Clearly, this only represents a beginning towards more complete quantitative modeling of the overall chemotaxis response, but it provides a platform to refine and explore other physical aspects of receptor packing that could tune the cellular response. The variations observed in chemoreceptor array position, localization and spatial distribution among different organisms thus suggest that each may have fine-tuned the organization of their signaling complexes to suit their respective signaling and motility requirements. The fact that the imaging of at least some components of whole cells at molecular resolution is now feasible and that it can be used to develop a testable predictive model is a potentially exciting advance, and lays the foundation for further quantitative studies of how the higher order organization of signaling complexes could be relevant to the regulation of function at the cellular level.